SOTAVerified

Autonomous Driving

Autonomous driving is the task of driving a vehicle without human conduction.

Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.

(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5526–5550 of 6092 papers

TitleStatusHype
Gaze Training by Modulated Dropout Improves Imitation Learning—0
GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction—0
GDM4MMIMO: Generative Diffusion Models for Massive MIMO Communications—0
Uncertainty Estimation Using Riemannian Model~Dynamics for Offline Reinforcement Learning—0
GenDDS: Generating Diverse Driving Video Scenarios with Prompt-to-Video Generative Model—0
GenDepth: Generalizing Monocular Depth Estimation for Arbitrary Camera Parameters via Ground Plane Embedding—0
General Intelligent Imaging and Uncertainty Quantification by Deterministic Diffusion Model—0
Generalizable Reinforcement Learning with Biologically Inspired Hyperdimensional Occupancy Grid Maps for Exploration and Goal-Directed Path Planning—0
Generalization of Agent Behavior through Explicit Representation of Context—0
Generalized dynamic cognitive hierarchy models for strategic driving behavior—0
Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving—0
Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networks—0
Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline—0
Generalizing Cooperative Eco-driving via Multi-residual Task Learning—0
PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving—0
Generating and Evolving Reward Functions for Highway Driving with Large Language Models—0
Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks—0
Generating Critical Scenarios for Testing Automated Driving Systems—0
Generating Multimodal Driving Scenes via Next-Scene Prediction—0
Generating Out-Of-Distribution Scenarios Using Language Models—0
Generating Semantic Adversarial Examples with Differentiable Rendering—0
Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments—0
Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications—0
Generative AI for Autonomous Driving: A Review—0
Generative Diffusion-based Contract Design for Efficient AI Twins Migration in Vehicular Embodied AI Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ReasonNetDriving Score79.95—Unverified
2InterFuserDriving Score76.18—Unverified
3TCPDriving Score75.14—Unverified
4TF++ WPDriving Score66.32—Unverified
5Learning From All Vehicles (LAV)Driving Score61.85—Unverified
6TransFuserDriving Score61.18—Unverified
7TransFuser (Reproduced)Driving Score55.04—Unverified
8TCP (Reproduced)Driving Score47.91—Unverified
9Latent TransFuserDriving Score45.2—Unverified
10GRIADDriving Score36.79—Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC69.17—Unverified
2TransFuserRC56.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC86.91—Unverified
2TransFuserRC78.41—Unverified